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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
nsql-350M-finetuned
This model is a fine-tuned version of NumbersStation/nsql-350M on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0114
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1122 | 1.0 | 22 | 0.0555 |
| 0.0564 | 2.0 | 44 | 0.0263 |
| 0.0368 | 3.0 | 66 | 0.0222 |
| 0.0266 | 4.0 | 88 | 0.0161 |
| 0.0267 | 5.0 | 110 | 0.0175 |
| 0.0195 | 6.0 | 132 | 0.0153 |
| 0.0165 | 7.0 | 154 | 0.0119 |
| 0.0145 | 8.0 | 176 | 0.0116 |
| 0.0127 | 9.0 | 198 | 0.0123 |
| 0.0131 | 10.0 | 220 | 0.0127 |
| 0.0122 | 11.0 | 242 | 0.0108 |
| 0.0114 | 12.0 | 264 | 0.0112 |
| 0.0103 | 13.0 | 286 | 0.0113 |
| 0.0099 | 14.0 | 308 | 0.0114 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for ckandemir/nsql-350M-finetuned
Base model
NumbersStation/nsql-350M